Intelligent Vehicles Lab
Our main research focuses are reinforcement learning, energy management for hybrid vehicles, and behavioral decision-making for automated vehicles. At present, we have one PI, two PhDs and eight masters in our laboratory.
People
Principal Investigator:
Yuan Lin(林远):
Yuan Lin (Member, IEEE) received the B.E. degree in civil engineering from Nanchang University, Nanchang, China, in 2011 and the Ph.D. degree in engineering mechanics from Virginia Tech, Blacksburg, VA, USA, in 2016. He was a Postdoctoral Fellow with Mechanical Engineering Department, Virginia Tech, from 2016 to 2018, and was with the Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada, from 2018 to 2020. He is currently an Assistant Professor with the Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
PhD Students:
Changfu Gong(龚长富): September 2021 - Present
Research Interests: Reinforcement Learning, Energy Management for Hybrid Electric Vehicles.
Jinming Xu(徐锦明): September 2021 - Present
Research Interests: Mixed-Integer Control, Safe Reinforcement Learning.
Master Students:
Xiao Liu(刘啸): September 2021 - Present
Research Interests: Reinforcement Learning, Intelligent Lane Change, Optimal Control.
Qitao Li(黎启涛): September 2022 - Present
Research Interests: Adaptive Cruise Control, Hybrid Electric Vehicles.
Ruichen Xu(徐瑞辰): September 2022 - Present
Research Interests: Reinforcement Learning.
Haonan Wu(吴浩楠): September 2022 - Present
Research Interests: Reinforcement Learning.
Antai Xie(谢安泰): September 2022 - Present
Research Interests: Deep Learning.
Zishun Zheng(郑子顺): September 2023 - Present
Research Interests: Autonomous Lane Change of Intelligent Vehicle.
Liyao Wang(王立尧): September 2023 - Present
Research Interests: Reinforcement Learning.
Zifeng Chen(陈梓锋): September 2024 - Present
Research Interests: Intelligent Vehicle.
Zhenning Zhu(朱振宁): September 2024 - Present
Research Interests: Reinforcement Learning.
Jianan Ji(纪家楠): September 2024 - Present
Research Interests: Reinforcement Learning.
Publications
- Xu R, Liu X, Xu J, et al. Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change[J]. arXiv preprint arXiv:2403.00446, 2024.
- Lin Y, Liu X, Zheng Z, et al. Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space[J]. arXiv preprint arXiv:2402.15790, 2024.
- Zheng Z, Liu X, Lin Y. Highway Discretionary Lane-change Decision and Control Using Model Predictive Control[J]. arXiv preprint arXiv:2402.17524, 2024.
- Lin Y, Xie A, Liu X. Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization[J]. arXiv preprint arXiv:2403.02882, 2024.
- Xu J, Lin Y. Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning[J]. Mathematics, 2024, 12(5): 663.
- Gong C, Xu J, Lin Y. Energy Management for a DM-i Plug-in Hybrid Electric Vehicle via Continuous-Discrete Reinforcement Learning[J]. arXiv preprint arXiv:2306.08823, 2023.
- Xu J, Lin Y. Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Vehicle Energy Management[J]. arXiv preprint arXiv:2305.01461, 2023.
- Li Q, Gong C, Lin Y. Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control[C]//2023 42st Chinese Control Conference (CCC). IEEE, 2023:6557-6562.
- Lin Y, McPhee J, Azad N L. Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning[J]. IEEE Transactions on Vehicular Technology, 2022, 71(7): 6958-6968.
- Lin Y, J. McPhee and N. L. Azad, “Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control,” in IEEE Transactions on Intelligent Vehicles, vol. 6, no. 2, pp. 221-231, June 2021, doi: 10.1109/TIV.2020.3012947.
- Lin Y, Azim Eskandarian, 2020: “Integrating Inter-Vehicular Communications, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss,” SAE International Journal of Connected and Automated Vehicles, 3(3), 193-204.
Facilities
The Driving Simulator System
- Vehicle in the loop.
Autonomous Delivery Vehicle
- Level 4 in complex road conditions.
Autonomous Robot
- ROS Robot.